Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations8920
Missing cells41558
Missing cells (%)19.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 MiB
Average record size in memory2.1 KiB

Variable types

Numeric8
Text4
Categorical4
Boolean7
Unsupported1

Alerts

garage has constant value "False" Constant
Etat is highly overall correlated with bathroomsHigh correlation
age is highly overall correlated with bathroomsHigh correlation
air_conditioning is highly overall correlated with heatingHigh correlation
bathrooms is highly overall correlated with Etat and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with rooms and 1 other fieldsHigh correlation
delegation is highly overall correlated with stateHigh correlation
floor is highly overall correlated with rez_de_chaussee_countHigh correlation
heating is highly overall correlated with air_conditioningHigh correlation
price is highly overall correlated with surfaceHigh correlation
rez_de_chaussee_count is highly overall correlated with floorHigh correlation
rooms is highly overall correlated with bedrooms and 1 other fieldsHigh correlation
state is highly overall correlated with delegationHigh correlation
surface is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
location has 3347 (37.5%) missing values Missing
rooms has 4686 (52.5%) missing values Missing
etage_count has 1772 (19.9%) missing values Missing
rez_de_chaussee_count has 1772 (19.9%) missing values Missing
floor has 3722 (41.7%) missing values Missing
date has 6087 (68.2%) missing values Missing
floor_description has 4493 (50.4%) missing values Missing
Etat has 7831 (87.8%) missing values Missing
age has 7825 (87.7%) missing values Missing
price is highly skewed (γ1 = 33.62460124) Skewed
bathrooms is highly skewed (γ1 = 75.63710453) Skewed
date is an unsupported type, check if it needs cleaning or further analysis Unsupported
etage_count has 3388 (38.0%) zeros Zeros
rez_de_chaussee_count has 5931 (66.5%) zeros Zeros
floor has 1002 (11.2%) zeros Zeros

Reproduction

Analysis started2025-02-08 16:55:59.381985
Analysis finished2025-02-08 16:56:06.393909
Duration7.01 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

price
Real number (ℝ)

High correlation  Skewed 

Distinct847
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean817523.82
Minimum10666
Maximum5.85 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:06.442906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10666
5-th percentile110000
Q1185000
median280000
Q3420000
95-th percentile809839.45
Maximum5.85 × 108
Range5.8498933 × 108
Interquartile range (IQR)235000

Descriptive statistics

Standard deviation13278030
Coefficient of variation (CV)16.241765
Kurtosis1244.4586
Mean817523.82
Median Absolute Deviation (MAD)110000
Skewness33.624601
Sum7.2923125 × 109
Variance1.7630608 × 1014
MonotonicityNot monotonic
2025-02-08T17:56:06.508558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000 231
 
2.6%
180000 180
 
2.0%
220000 173
 
1.9%
350000 166
 
1.9%
230000 159
 
1.8%
150000 156
 
1.7%
240000 152
 
1.7%
200000 150
 
1.7%
320000 149
 
1.7%
170000 146
 
1.6%
Other values (837) 7258
81.4%
ValueCountFrequency (%)
10666 1
< 0.1%
11111 1
< 0.1%
12000 2
< 0.1%
15000 2
< 0.1%
18000 1
< 0.1%
19000 2
< 0.1%
20000 1
< 0.1%
22222 2
< 0.1%
23000 1
< 0.1%
24700 1
< 0.1%
ValueCountFrequency (%)
585000000 1
< 0.1%
570000000 1
< 0.1%
520000000 1
< 0.1%
330000000 2
< 0.1%
290000000 1
< 0.1%
280000000 2
< 0.1%
235000000 2
< 0.1%
230000000 1
< 0.1%
98218890 1
< 0.1%
80000000 1
< 0.1%

location
Text

Missing 

Distinct728
Distinct (%)13.1%
Missing3347
Missing (%)37.5%
Memory size498.3 KiB
2025-02-08T17:56:06.712410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length83
Median length71
Mean length14.899336
Min length0

Characters and Unicode

Total characters83034
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)6.9%

Sample

1st rowJardins de Carthage
2nd rowLes Jardins du lac 2
3rd rowLes berges du lac 1
4th rowEnnasr 1-2
5th rowAouina
ValueCountFrequency (%)
el 1354
 
8.9%
tunis 1310
 
8.6%
la 940
 
6.2%
cité 677
 
4.5%
soukra 581
 
3.8%
530
 
3.5%
aouina 478
 
3.2%
ariana 365
 
2.4%
ennasr 359
 
2.4%
carthage 308
 
2.0%
Other values (539) 8272
54.5%
2025-02-08T17:56:06.922898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10706
 
12.9%
10387
 
12.5%
n 5719
 
6.9%
i 5053
 
6.1%
u 4698
 
5.7%
e 4605
 
5.5%
r 4447
 
5.4%
o 3566
 
4.3%
s 3453
 
4.2%
l 3042
 
3.7%
Other values (87) 27358
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10706
 
12.9%
10387
 
12.5%
n 5719
 
6.9%
i 5053
 
6.1%
u 4698
 
5.7%
e 4605
 
5.5%
r 4447
 
5.4%
o 3566
 
4.3%
s 3453
 
4.2%
l 3042
 
3.7%
Other values (87) 27358
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10706
 
12.9%
10387
 
12.5%
n 5719
 
6.9%
i 5053
 
6.1%
u 4698
 
5.7%
e 4605
 
5.5%
r 4447
 
5.4%
o 3566
 
4.3%
s 3453
 
4.2%
l 3042
 
3.7%
Other values (87) 27358
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10706
 
12.9%
10387
 
12.5%
n 5719
 
6.9%
i 5053
 
6.1%
u 4698
 
5.7%
e 4605
 
5.5%
r 4447
 
5.4%
o 3566
 
4.3%
s 3453
 
4.2%
l 3042
 
3.7%
Other values (87) 27358
32.9%

state
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size479.4 KiB
ARIANA
3653 
TUNIS
3632 
BEN AROUS
1095 
MANOUBA
540 

Length

Max length9
Median length7
Mean length6.0216368
Min length5

Characters and Unicode

Total characters53713
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTUNIS
2nd rowTUNIS
3rd rowTUNIS
4th rowARIANA
5th rowARIANA

Common Values

ValueCountFrequency (%)
ARIANA 3653
41.0%
TUNIS 3632
40.7%
BEN AROUS 1095
 
12.3%
MANOUBA 540
 
6.1%

Length

2025-02-08T17:56:06.981902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T17:56:07.034897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ariana 3653
36.5%
tunis 3632
36.3%
ben 1095
 
10.9%
arous 1095
 
10.9%
manouba 540
 
5.4%

Most occurring characters

ValueCountFrequency (%)
A 13134
24.5%
N 8920
16.6%
I 7285
13.6%
U 5267
9.8%
R 4748
 
8.8%
S 4727
 
8.8%
T 3632
 
6.8%
B 1635
 
3.0%
O 1635
 
3.0%
E 1095
 
2.0%
Other values (2) 1635
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 13134
24.5%
N 8920
16.6%
I 7285
13.6%
U 5267
9.8%
R 4748
 
8.8%
S 4727
 
8.8%
T 3632
 
6.8%
B 1635
 
3.0%
O 1635
 
3.0%
E 1095
 
2.0%
Other values (2) 1635
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 13134
24.5%
N 8920
16.6%
I 7285
13.6%
U 5267
9.8%
R 4748
 
8.8%
S 4727
 
8.8%
T 3632
 
6.8%
B 1635
 
3.0%
O 1635
 
3.0%
E 1095
 
2.0%
Other values (2) 1635
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 13134
24.5%
N 8920
16.6%
I 7285
13.6%
U 5267
9.8%
R 4748
 
8.8%
S 4727
 
8.8%
T 3632
 
6.8%
B 1635
 
3.0%
O 1635
 
3.0%
E 1095
 
2.0%
Other values (2) 1635
 
3.0%

bedrooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4378924
Minimum0
Maximum8
Zeros39
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.075951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91747082
Coefficient of variation (CV)0.3763377
Kurtosis0.27890965
Mean2.4378924
Median Absolute Deviation (MAD)1
Skewness0.33318652
Sum21746
Variance0.84175271
MonotonicityNot monotonic
2025-02-08T17:56:07.125901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 3627
40.7%
3 3076
34.5%
1 1207
 
13.5%
4 810
 
9.1%
5 151
 
1.7%
0 39
 
0.4%
6 9
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 39
 
0.4%
1 1207
 
13.5%
2 3627
40.7%
3 3076
34.5%
4 810
 
9.1%
5 151
 
1.7%
6 9
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 9
 
0.1%
5 151
 
1.7%
4 810
 
9.1%
3 3076
34.5%
2 3627
40.7%
1 1207
 
13.5%
0 39
 
0.4%

rooms
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)0.4%
Missing4686
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean3.0292867
Minimum0
Maximum34
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.177901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum34
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4811492
Coefficient of variation (CV)0.48894323
Kurtosis146.27517
Mean3.0292867
Median Absolute Deviation (MAD)1
Skewness7.9895249
Sum12826
Variance2.1938031
MonotonicityNot monotonic
2025-02-08T17:56:07.229224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 1581
 
17.7%
4 1017
 
11.4%
2 964
 
10.8%
1 374
 
4.2%
5 235
 
2.6%
6 31
 
0.3%
7 15
 
0.2%
10 4
 
< 0.1%
0 3
 
< 0.1%
8 2
 
< 0.1%
Other values (6) 8
 
0.1%
(Missing) 4686
52.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 374
 
4.2%
2 964
10.8%
3 1581
17.7%
4 1017
11.4%
5 235
 
2.6%
6 31
 
0.3%
7 15
 
0.2%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
34 2
 
< 0.1%
32 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
14 1
 
< 0.1%
10 4
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 15
0.2%
6 31
0.3%

bathrooms
Real number (ℝ)

High correlation  Skewed 

Distinct9
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.2310109
Minimum0
Maximum130
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.275851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum130
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.471732
Coefficient of variation (CV)1.1955475
Kurtosis6582.8585
Mean1.2310109
Median Absolute Deviation (MAD)0
Skewness75.637105
Sum10972
Variance2.1659951
MonotonicityNot monotonic
2025-02-08T17:56:07.320851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 7270
81.5%
2 1366
 
15.3%
3 224
 
2.5%
4 27
 
0.3%
0 17
 
0.2%
5 6
 
0.1%
7 1
 
< 0.1%
23 1
 
< 0.1%
130 1
 
< 0.1%
(Missing) 7
 
0.1%
ValueCountFrequency (%)
0 17
 
0.2%
1 7270
81.5%
2 1366
 
15.3%
3 224
 
2.5%
4 27
 
0.3%
5 6
 
0.1%
7 1
 
< 0.1%
23 1
 
< 0.1%
130 1
 
< 0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
23 1
 
< 0.1%
7 1
 
< 0.1%
5 6
 
0.1%
4 27
 
0.3%
3 224
 
2.5%
2 1366
 
15.3%
1 7270
81.5%
0 17
 
0.2%

surface
Real number (ℝ)

High correlation 

Distinct330
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.00861
Minimum16
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.377851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile57
Q190
median115
Q3150
95-th percentile222.05
Maximum780
Range764
Interquartile range (IQR)60

Descriptive statistics

Standard deviation57.364709
Coefficient of variation (CV)0.45888605
Kurtosis12.314809
Mean125.00861
Median Absolute Deviation (MAD)30
Skewness2.3177452
Sum1115076.8
Variance3290.7099
MonotonicityNot monotonic
2025-02-08T17:56:07.448991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 458
 
5.1%
120 299
 
3.4%
90 290
 
3.3%
110 255
 
2.9%
80 227
 
2.5%
150 196
 
2.2%
110.3542835 192
 
2.2%
130 179
 
2.0%
140 168
 
1.9%
70 166
 
1.9%
Other values (320) 6490
72.8%
ValueCountFrequency (%)
16 1
 
< 0.1%
18 2
 
< 0.1%
20 4
 
< 0.1%
25 2
 
< 0.1%
26 5
 
0.1%
27 1
 
< 0.1%
28 2
 
< 0.1%
30 13
0.1%
31 3
 
< 0.1%
32 4
 
< 0.1%
ValueCountFrequency (%)
780 1
< 0.1%
700 1
< 0.1%
658 1
< 0.1%
650 1
< 0.1%
623 1
< 0.1%
614 2
< 0.1%
600 2
< 0.1%
585 1
< 0.1%
583 1
< 0.1%
580 1
< 0.1%

parking
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
True
4711 
False
4209 
ValueCountFrequency (%)
True 4711
52.8%
False 4209
47.2%
2025-02-08T17:56:07.496991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
7293 
True
1627 
ValueCountFrequency (%)
False 7293
81.8%
True 1627
 
18.2%
2025-02-08T17:56:07.522991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

balcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
True
5137 
False
3783 
ValueCountFrequency (%)
True 5137
57.6%
False 3783
42.4%
2025-02-08T17:56:07.547991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
True
4639 
False
4281 
ValueCountFrequency (%)
True 4639
52.0%
False 4281
48.0%
2025-02-08T17:56:07.572991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

air_conditioning
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
4954 
True
3966 
ValueCountFrequency (%)
False 4954
55.5%
True 3966
44.5%
2025-02-08T17:56:07.599991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

garage
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
8920 
ValueCountFrequency (%)
False 8920
100.0%
2025-02-08T17:56:07.623991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

elevator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
False
5886 
True
3034 
ValueCountFrequency (%)
False 5886
66.0%
True 3034
34.0%
2025-02-08T17:56:07.644991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

etage_count
Real number (ℝ)

Missing  Zeros 

Distinct8
Distinct (%)0.1%
Missing1772
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean0.64255736
Minimum0
Maximum12
Zeros3388
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.684176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72895444
Coefficient of variation (CV)1.1344582
Kurtosis11.241025
Mean0.64255736
Median Absolute Deviation (MAD)1
Skewness1.7220175
Sum4593
Variance0.53137457
MonotonicityNot monotonic
2025-02-08T17:56:07.733204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3388
38.0%
1 3072
34.4%
2 590
 
6.6%
3 71
 
0.8%
4 19
 
0.2%
6 5
 
0.1%
5 2
 
< 0.1%
12 1
 
< 0.1%
(Missing) 1772
19.9%
ValueCountFrequency (%)
0 3388
38.0%
1 3072
34.4%
2 590
 
6.6%
3 71
 
0.8%
4 19
 
0.2%
5 2
 
< 0.1%
6 5
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
6 5
 
0.1%
5 2
 
< 0.1%
4 19
 
0.2%
3 71
 
0.8%
2 590
 
6.6%
1 3072
34.4%
0 3388
38.0%

rez_de_chaussee_count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)0.1%
Missing1772
Missing (%)19.9%
Infinite0
Infinite (%)0.0%
Mean0.2112479
Minimum0
Maximum6
Zeros5931
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size69.8 KiB
2025-02-08T17:56:07.774761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52381586
Coefficient of variation (CV)2.4796263
Kurtosis14.360295
Mean0.2112479
Median Absolute Deviation (MAD)0
Skewness3.197739
Sum1510
Variance0.27438305
MonotonicityNot monotonic
2025-02-08T17:56:07.818189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5931
66.5%
1 987
 
11.1%
2 189
 
2.1%
3 25
 
0.3%
4 11
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
(Missing) 1772
 
19.9%
ValueCountFrequency (%)
0 5931
66.5%
1 987
 
11.1%
2 189
 
2.1%
3 25
 
0.3%
4 11
 
0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
< 0.1%
4 11
 
0.1%
3 25
 
0.3%
2 189
 
2.1%
1 987
 
11.1%
0 5931
66.5%
Distinct226
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
2025-02-08T17:56:07.948045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length22
Mean length10.14787
Min length5

Characters and Unicode

Total characters90519
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st rowJARDINS DE CARTHAGE
2nd rowLES JARDINS
3rd rowBERGE DU LAC
4th rowENNASR
5th rowAOUINA
ValueCountFrequency (%)
el 1462
 
8.7%
aouina 873
 
5.2%
soukra 818
 
4.9%
ennasr 664
 
4.0%
jardins 592
 
3.5%
zaghouan 561
 
3.4%
ain 561
 
3.4%
carthage 482
 
2.9%
de 468
 
2.8%
menzah 433
 
2.6%
Other values (229) 9807
58.7%
2025-02-08T17:56:08.141361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 13951
15.4%
E 8170
 
9.0%
7801
 
8.6%
R 6775
 
7.5%
N 6720
 
7.4%
O 5195
 
5.7%
I 4845
 
5.4%
U 4808
 
5.3%
L 4031
 
4.5%
S 3585
 
4.0%
Other values (28) 24638
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 13951
15.4%
E 8170
 
9.0%
7801
 
8.6%
R 6775
 
7.5%
N 6720
 
7.4%
O 5195
 
5.7%
I 4845
 
5.4%
U 4808
 
5.3%
L 4031
 
4.5%
S 3585
 
4.0%
Other values (28) 24638
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 13951
15.4%
E 8170
 
9.0%
7801
 
8.6%
R 6775
 
7.5%
N 6720
 
7.4%
O 5195
 
5.7%
I 4845
 
5.4%
U 4808
 
5.3%
L 4031
 
4.5%
S 3585
 
4.0%
Other values (28) 24638
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 13951
15.4%
E 8170
 
9.0%
7801
 
8.6%
R 6775
 
7.5%
N 6720
 
7.4%
O 5195
 
5.7%
I 4845
 
5.4%
U 4808
 
5.3%
L 4031
 
4.5%
S 3585
 
4.0%
Other values (28) 24638
27.2%

delegation
Categorical

High correlation 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size511.7 KiB
SOUKRA
2181 
MARSA
1704 
EL MNIHLA
665 
ARIANA VILLE
533 
BAB BHAR
 
305
Other values (41)
3532 

Length

Max length29
Median length21
Mean length8.1378924
Min length4

Characters and Unicode

Total characters72590
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMARSA
2nd rowBAB BHAR
3rd rowMARSA
4th rowEL MNIHLA
5th rowSOUKRA

Common Values

ValueCountFrequency (%)
SOUKRA 2181
24.5%
MARSA 1704
19.1%
EL MNIHLA 665
 
7.5%
ARIANA VILLE 533
 
6.0%
BAB BHAR 305
 
3.4%
EL MENZAH 298
 
3.3%
MANOUBA 282
 
3.2%
RADES 279
 
3.1%
RAOUED 245
 
2.7%
BARDO 227
 
2.5%
Other values (36) 2201
24.7%

Length

2025-02-08T17:56:08.205800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
soukra 2181
16.3%
el 2136
15.9%
marsa 1704
 
12.7%
mnihla 665
 
5.0%
ariana 533
 
4.0%
ville 533
 
4.0%
bab 351
 
2.6%
bhar 305
 
2.3%
menzah 298
 
2.2%
manouba 282
 
2.1%
Other values (53) 4414
32.9%

Most occurring characters

ValueCountFrequency (%)
A 13621
18.8%
R 6926
9.5%
E 5733
 
7.9%
L 5040
 
6.9%
S 4639
 
6.4%
4484
 
6.2%
O 4358
 
6.0%
U 4061
 
5.6%
M 4023
 
5.5%
I 3455
 
4.8%
Other values (16) 16250
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 13621
18.8%
R 6926
9.5%
E 5733
 
7.9%
L 5040
 
6.9%
S 4639
 
6.4%
4484
 
6.2%
O 4358
 
6.0%
U 4061
 
5.6%
M 4023
 
5.5%
I 3455
 
4.8%
Other values (16) 16250
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 13621
18.8%
R 6926
9.5%
E 5733
 
7.9%
L 5040
 
6.9%
S 4639
 
6.4%
4484
 
6.2%
O 4358
 
6.0%
U 4061
 
5.6%
M 4023
 
5.5%
I 3455
 
4.8%
Other values (16) 16250
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 13621
18.8%
R 6926
9.5%
E 5733
 
7.9%
L 5040
 
6.9%
S 4639
 
6.4%
4484
 
6.2%
O 4358
 
6.0%
U 4061
 
5.6%
M 4023
 
5.5%
I 3455
 
4.8%
Other values (16) 16250
22.4%

floor
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct16
Distinct (%)0.3%
Missing3722
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean2.2052713
Minimum-1
Maximum20
Zeros1002
Zeros (%)11.2%
Negative1
Negative (%)< 0.1%
Memory size69.8 KiB
2025-02-08T17:56:08.253656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum20
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9228919
Coefficient of variation (CV)0.87195253
Kurtosis3.7234158
Mean2.2052713
Median Absolute Deviation (MAD)1
Skewness1.2581369
Sum11463
Variance3.6975131
MonotonicityNot monotonic
2025-02-08T17:56:08.301768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 1254
 
14.1%
2 1036
 
11.6%
0 1002
 
11.2%
3 729
 
8.2%
4 559
 
6.3%
5 255
 
2.9%
6 203
 
2.3%
7 114
 
1.3%
8 21
 
0.2%
9 12
 
0.1%
Other values (6) 13
 
0.1%
(Missing) 3722
41.7%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 1002
11.2%
1 1254
14.1%
2 1036
11.6%
3 729
8.2%
4 559
6.3%
5 255
 
2.9%
6 203
 
2.3%
7 114
 
1.3%
8 21
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
11 1
 
< 0.1%
10 8
 
0.1%
9 12
 
0.1%
8 21
 
0.2%
7 114
1.3%
6 203
2.3%
5 255
2.9%
Distinct8904
Distinct (%)100.0%
Missing16
Missing (%)0.2%
Memory size11.0 MiB
2025-02-08T17:56:08.535768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5229
Median length1123
Mean length494.12882
Min length9

Characters and Unicode

Total characters4399723
Distinct characters571
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8904 ?
Unique (%)100.0%

Sample

1st rowGreen Immobilière vous propose à la vente un bel appartement dans une résidence sécurisée et proche de toutes commodités a el nasr 2. Il est composé : -un salon salle à manger avec terrasse -une salle d'eau -une cuisine équipée avec séchoir -deux chambres à coucher avec une salle de bain commune -une suite parentale -une place de parking au sous-sol Prix demandé : 400.000 MDT
2nd rowL'agence immobilière Good Life vous propose à la vente un appartement S+3, qui occupe le premier étage d’un immeuble R+5 avec double ascenseur, situé à Jardins d’El Menzah. Cet appartement, construit en 2006, offre une superficie totale de 157 (140 net). Il se compose d’un salon lumineux avec un grand balcon, de trois chambres à coucher équipées de dressings, d’une salle de bain, d’une salle d’eau, d’un dressing supplémentaire dans le couloir, ainsi que d’une cuisine avec séchoir. Une place de parking est incluse au sous-sol. L’appartement est équipé de chauffage central, de deux climatiseurs et d’un interphone. La résidence est sécurisée et bénéficie d’un service de gardiennage. Papier: TF en cours. Le prix demandé est de 365 000 dinars. Pour plus d’informations, veuillez contacter votre courtier en immobilier, Mr Slim Dhouib.
3rd rowLa Croisette Immobilière Tunisie vous propose à la vente un appartement S+1 occupant le rez-de-chaussée d’une résidence gardée à La Nouvelle Soukra, à Ain Zaghouan. Ce dernier fait 77 mètres carrés et se compose d'un grand salon, d'une cuisine aménagée et équipée, d'une chambre à coucher avec placard attenante à une terrasse et d’une salle d'eau avec douche. L'appartement est équipé du chauffage central et de climatiseurs. Réf :MAV1783
4th rowA vendre un appartement de 83 net au titre foncier à l ariana essoughra tout prés de l université ESPRIT Ariana au 2ème étage dans une résidence prés de toutes commodités composé d un salon cuisine bien équipée deux chambres et sdb prix négociable.
5th rowL'agence immobilière Good Life vous propose à la vente un S+2 à la Soukra côté Carrefour express. Cet appartement de 143 au 7ème étage étage, se compose d’un spacieux hall d’entrée, d’une cuisine équipée de plaque , Hotte et four avec séchoir, d’un salon/ salle à manger ouvrant sur une petite terrasse, d’une salle d’eau pour les invités avec aération fenêtre, de deux chambres qui se partageant une salle de bain ainsi qu'une terrasse. L’appartement dispose d'un chauffage central et de 4 climatiseurs. Une place de parking est à disposition. Pour une visite ou plus d'informations contacter votre courtier en immobilier Myriam Zahaf.
ValueCountFrequency (%)
de 31119
 
4.3%
à 22798
 
3.2%
19474
 
2.7%
et 19008
 
2.6%
une 18209
 
2.5%
un 16424
 
2.3%
avec 14125
 
2.0%
appartement 10594
 
1.5%
salle 10577
 
1.5%
la 10375
 
1.4%
Other values (23219) 548452
76.1%
2025-02-08T17:56:08.779476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
712435
16.2%
e 482689
 
11.0%
a 277548
 
6.3%
n 257439
 
5.9%
s 226704
 
5.2%
i 219979
 
5.0%
t 210749
 
4.8%
r 209859
 
4.8%
u 192216
 
4.4%
o 153848
 
3.5%
Other values (561) 1456257
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4399723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
712435
16.2%
e 482689
 
11.0%
a 277548
 
6.3%
n 257439
 
5.9%
s 226704
 
5.2%
i 219979
 
5.0%
t 210749
 
4.8%
r 209859
 
4.8%
u 192216
 
4.4%
o 153848
 
3.5%
Other values (561) 1456257
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4399723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
712435
16.2%
e 482689
 
11.0%
a 277548
 
6.3%
n 257439
 
5.9%
s 226704
 
5.2%
i 219979
 
5.0%
t 210749
 
4.8%
r 209859
 
4.8%
u 192216
 
4.4%
o 153848
 
3.5%
Other values (561) 1456257
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4399723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
712435
16.2%
e 482689
 
11.0%
a 277548
 
6.3%
n 257439
 
5.9%
s 226704
 
5.2%
i 219979
 
5.0%
t 210749
 
4.8%
r 209859
 
4.8%
u 192216
 
4.4%
o 153848
 
3.5%
Other values (561) 1456257
33.1%

date
Unsupported

Missing  Rejected  Unsupported 

Missing6087
Missing (%)68.2%
Memory size397.1 KiB

floor_description
Text

Missing 

Distinct3458
Distinct (%)78.1%
Missing4493
Missing (%)50.4%
Memory size845.7 KiB
2025-02-08T17:56:08.929616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6479
Median length899
Mean length74.691665
Min length15

Characters and Unicode

Total characters330660
Distinct characters113
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2934 ?
Unique (%)66.3%

Sample

1st rows+3 occupant le rez-de-chaussee d'une residence gardee
2nd rowà coucher qui partage une salle de
3rd rowun etage de villa situe
4th rowluxe au dernier etage avec une vue
5th rowoccupant le dernier etage d'une residence gardee
ValueCountFrequency (%)
etage 4765
 
8.2%
au 4597
 
8.0%
residence 1522
 
2.6%
une 1516
 
2.6%
avec 1476
 
2.6%
1406
 
2.4%
situe 1308
 
2.3%
rdc 1259
 
2.2%
de 1253
 
2.2%
dans 1021
 
1.8%
Other values (2260) 37683
65.2%
2025-02-08T17:56:09.159318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 57970
17.5%
53379
16.1%
a 27074
 
8.2%
s 18637
 
5.6%
u 18194
 
5.5%
t 17208
 
5.2%
r 16017
 
4.8%
n 15876
 
4.8%
i 12119
 
3.7%
d 11849
 
3.6%
Other values (103) 82337
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 330660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57970
17.5%
53379
16.1%
a 27074
 
8.2%
s 18637
 
5.6%
u 18194
 
5.5%
t 17208
 
5.2%
r 16017
 
4.8%
n 15876
 
4.8%
i 12119
 
3.7%
d 11849
 
3.6%
Other values (103) 82337
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 330660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57970
17.5%
53379
16.1%
a 27074
 
8.2%
s 18637
 
5.6%
u 18194
 
5.5%
t 17208
 
5.2%
r 16017
 
4.8%
n 15876
 
4.8%
i 12119
 
3.7%
d 11849
 
3.6%
Other values (103) 82337
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 330660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57970
17.5%
53379
16.1%
a 27074
 
8.2%
s 18637
 
5.6%
u 18194
 
5.5%
t 17208
 
5.2%
r 16017
 
4.8%
n 15876
 
4.8%
i 12119
 
3.7%
d 11849
 
3.6%
Other values (103) 82337
24.9%

Etat
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.8%
Missing7831
Missing (%)87.8%
Memory size503.4 KiB
nouveau
363 
bon état
351 
Bon état
221 
Nouveau
136 
à rénover
 
12
Other values (4)
 
6

Length

Max length9
Median length8
Mean length7.5417815
Min length4

Characters and Unicode

Total characters8213
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowNouveau
2nd rownouveau
3rd rownouveau
4th rownouveau
5th rowbon état

Common Values

ValueCountFrequency (%)
nouveau 363
 
4.1%
bon état 351
 
3.9%
Bon état 221
 
2.5%
Nouveau 136
 
1.5%
à rénover 12
 
0.1%
À rénover 2
 
< 0.1%
vide 2
 
< 0.1%
neuf é 1
 
< 0.1%
Neuf 1
 
< 0.1%
(Missing) 7831
87.8%

Length

2025-02-08T17:56:09.219851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T17:56:09.273919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bon 572
34.1%
état 572
34.1%
nouveau 499
29.8%
à 14
 
0.8%
rénover 14
 
0.8%
vide 2
 
0.1%
neuf 2
 
0.1%
é 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 1144
13.9%
o 1085
13.2%
a 1071
13.0%
u 1000
12.2%
n 950
11.6%
587
7.1%
é 587
7.1%
e 517
6.3%
v 515
6.3%
b 351
 
4.3%
Other values (8) 406
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1144
13.9%
o 1085
13.2%
a 1071
13.0%
u 1000
12.2%
n 950
11.6%
587
7.1%
é 587
7.1%
e 517
6.3%
v 515
6.3%
b 351
 
4.3%
Other values (8) 406
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1144
13.9%
o 1085
13.2%
a 1071
13.0%
u 1000
12.2%
n 950
11.6%
587
7.1%
é 587
7.1%
e 517
6.3%
v 515
6.3%
b 351
 
4.3%
Other values (8) 406
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1144
13.9%
o 1085
13.2%
a 1071
13.0%
u 1000
12.2%
n 950
11.6%
587
7.1%
é 587
7.1%
e 517
6.3%
v 515
6.3%
b 351
 
4.3%
Other values (8) 406
 
4.9%

age
Categorical

High correlation  Missing 

Distinct9
Distinct (%)0.8%
Missing7825
Missing (%)87.7%
Memory size492.8 KiB
10-20 years
269 
Less than a year
240 
5-10 years
238 
1-5 years
191 
20-30 years
98 
Other values (4)
59 

Length

Max length19
Median length16
Mean length11.562557
Min length9

Characters and Unicode

Total characters12661
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLess than a year
2nd rowLess than a year
3rd rowLess than a year
4th row10-20 years
5th rowLess than a year

Common Values

ValueCountFrequency (%)
10-20 years 269
 
3.0%
Less than a year 240
 
2.7%
5-10 years 238
 
2.7%
1-5 years 191
 
2.1%
20-30 years 98
 
1.1%
30-50 years 45
 
0.5%
50-70 years 6
 
0.1%
70-100 years 4
 
< 0.1%
More than 100 years 4
 
< 0.1%
(Missing) 7825
87.7%

Length

2025-02-08T17:56:09.340854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T17:56:09.389848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
years 855
31.9%
10-20 269
 
10.0%
than 244
 
9.1%
less 240
 
9.0%
a 240
 
9.0%
year 240
 
9.0%
5-10 238
 
8.9%
1-5 191
 
7.1%
20-30 98
 
3.7%
30-50 45
 
1.7%
Other values (4) 18
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1583
12.5%
a 1579
12.5%
e 1339
10.6%
s 1335
10.5%
r 1099
8.7%
y 1095
8.6%
0 1094
8.6%
- 851
6.7%
1 706
5.6%
5 480
 
3.8%
Other values (9) 1500
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1583
12.5%
a 1579
12.5%
e 1339
10.6%
s 1335
10.5%
r 1099
8.7%
y 1095
8.6%
0 1094
8.6%
- 851
6.7%
1 706
5.6%
5 480
 
3.8%
Other values (9) 1500
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1583
12.5%
a 1579
12.5%
e 1339
10.6%
s 1335
10.5%
r 1099
8.7%
y 1095
8.6%
0 1094
8.6%
- 851
6.7%
1 706
5.6%
5 480
 
3.8%
Other values (9) 1500
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1583
12.5%
a 1579
12.5%
e 1339
10.6%
s 1335
10.5%
r 1099
8.7%
y 1095
8.6%
0 1094
8.6%
- 851
6.7%
1 706
5.6%
5 480
 
3.8%
Other values (9) 1500
11.8%

Interactions

2025-02-08T17:56:05.531675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.437296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.100826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.762718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.374281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.025814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.536974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.002136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.584743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.506679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.170944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.834645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.437957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.089311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.594719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.060153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.637515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.632303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.242587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.903038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.503566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.158225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.653236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.118153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.696449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.698210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.317053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.968135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.579847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.225960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.710888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.180152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.749471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.766350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.392676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.037305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.744267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.292362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.770746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.236326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.807751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.856212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.480259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.145082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.815794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.361933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.835274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.370912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.857525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:01.954488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.605516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.225111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.889758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.422930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.892044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.423675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.913548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.039817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:02.699604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.298699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:03.960549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.482939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:04.948573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-08T17:56:05.478675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-08T17:56:09.461848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Etatageair_conditioningbalconybathroomsbedroomsdelegationelevatorequipped_kitchenetage_countfloorheatingparkingpricerez_de_chaussee_countroomsstatesurface
Etat1.0000.3570.3300.2391.0000.0930.3610.2280.3100.0000.0630.3360.1940.0000.0000.0000.4560.033
age0.3571.0000.2660.1051.0000.1340.2950.3810.3840.1560.0690.3560.2590.0660.0540.0000.2170.000
air_conditioning0.3300.2661.0000.3060.0000.1290.3350.3420.4180.0770.1030.7010.3370.0000.0490.0250.2000.140
balcony0.2390.1050.3061.0000.0000.1460.1840.2130.2560.0950.1050.2820.2130.0140.0300.0660.0980.181
bathrooms1.0001.0000.0000.0001.0000.3070.0000.0070.000-0.0280.0550.0040.0000.322-0.0000.2310.0120.350
bedrooms0.0930.1340.1290.1460.3071.0000.1000.0890.1300.1170.0840.1250.0860.370-0.0050.6890.0820.641
delegation0.3610.2950.3350.1840.0000.1001.0000.2320.1850.0440.1050.3130.2140.0000.0480.0980.9980.097
elevator0.2280.3810.3420.2130.0070.0890.2321.0000.4270.0000.2620.3890.3050.0180.1190.0380.1370.081
equipped_kitchen0.3100.3840.4180.2560.0000.1300.1850.4271.0000.0380.0790.3400.2300.0000.0000.1420.1380.087
etage_count0.0000.1560.0770.095-0.0280.1170.0440.0000.0381.0000.4760.0800.0290.001-0.1590.0750.0240.065
floor0.0630.0690.1030.1050.0550.0840.1050.2620.0790.4761.0000.1160.053-0.054-0.7440.0560.0700.008
heating0.3360.3560.7010.2820.0040.1250.3130.3890.3400.0800.1161.0000.3130.0090.0290.0160.1900.130
parking0.1940.2590.3370.2130.0000.0860.2140.3050.2300.0290.0530.3131.0000.0000.0080.0480.0920.124
price0.0000.0660.0000.0140.3220.3700.0000.0180.0000.001-0.0540.0090.0001.0000.0440.2720.0000.710
rez_de_chaussee_count0.0000.0540.0490.030-0.000-0.0050.0480.1190.000-0.159-0.7440.0290.0080.0441.000-0.0390.0100.029
rooms0.0000.0000.0250.0660.2310.6890.0980.0380.1420.0750.0560.0160.0480.272-0.0391.0000.0600.521
state0.4560.2170.2000.0980.0120.0820.9980.1370.1380.0240.0700.1900.0920.0000.0100.0601.0000.082
surface0.0330.0000.1400.1810.3500.6410.0970.0810.0870.0650.0080.1300.1240.7100.0290.5210.0821.000

Missing values

2025-02-08T17:56:06.011556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-08T17:56:06.136301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-08T17:56:06.300906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pricelocationstatebedroomsroomsbathroomssurfaceparkingequipped_kitchenbalconyheatingair_conditioninggarageelevatoretage_countrez_de_chaussee_countmunicipalitydelegationfloordescriptiondatefloor_descriptionEtatage
0680000Jardins de CarthageTUNIS3.04.01.0150.0YesYesnonononoNo0.00.0JARDINS DE CARTHAGEMARSANaNNaNNaNNaNNaNNaN
1560000Les Jardins du lac 2TUNIS2.03.01.0125.0YesYesYesnoYesnoYesNaNNaNLES JARDINSBAB BHAR7.0NaNNaNNaNNaNNaN
2480000Les berges du lac 1TUNIS3.05.02.0109.0YesnononononoNo0.00.0BERGE DU LACMARSANaNNaNNaNNaNNaNNaN
3410000Ennasr 1-2ARIANA3.04.02.0130.0YesnononononoNo0.00.0ENNASREL MNIHLANaNNaNNaNNaNNaNNaN
4399000AouinaARIANA2.03.02.0115.0nonononononoNo0.00.0AOUINASOUKRANaNNaNNaNNaNNaNNaN
5395000La SoukraARIANA3.05.01.0114.0YesYesYesnoYesnoYes0.00.0SOUKRASOUKRANaNNaNNaNNaNNaNNaN
6385000La SoukraARIANA2.03.0NaN120.0nonononononoNo0.00.0SOUKRASOUKRANaNNaNNaNNaNNaNNaN
7385000La SoukraARIANA2.03.02.091.0YesYesYesnononoYesNaNNaNSOUKRASOUKRA0.0NaNNaNNaNNaNNaN
8375000ChotranaARIANA2.03.01.092.0nonononononoNo0.00.0CHOTRANASOUKRANaNNaNNaNNaNNaNNaN
9360000Ennasr 1-2ARIANA2.04.02.090.0YesYesYesYesnonoYesNaNNaNENNASREL MNIHLA7.0NaNNaNNaNNaNNaN
pricelocationstatebedroomsroomsbathroomssurfaceparkingequipped_kitchenbalconyheatingair_conditioninggarageelevatoretage_countrez_de_chaussee_countmunicipalitydelegationfloordescriptiondatefloor_descriptionEtatage
8910210000NaNTUNIS4.05.01.0110.0nonononononoNoNaNNaNCITE EL KHADRACITE EL KHADRA/CITÉ EL KHADRA3.0<p> L'appartement S+4 de 110m2 se trouve au 3ème étage d'un immeuble à la Cité Olympique. L'appartement bénéficie d'une belle luminosité naturelle à cause de l'orientation. Il est constitué de quatre chambres à coucher,d'un salon spacieux qui offre également une vue dégagée sur les environs, une cuisine, une salle de bain, un point d'eau.</p><p>Il propose un espace de vie pratique comprenant toutes les commodités à proximité.</p> 5 piècesNaNNaNNaN30-50 years
8911600000NaNARIANA3.05.01.0306.0YesnoYesYesYesnoYesNaNNaNENNASREL MNIHLA6.0<p><strong>Ce duplex</strong> s'étend sur le <strong>sixième</strong> et le <strong>septième</strong> étage d'une résidence <strong>dotée</strong> d’un ascenseur. L'entrée mène à un salon <strong>lumineux</strong>, grâces à ses nombreuses fenêtres offrant une <strong>vue</strong> <strong>panoramique</strong>, ainsi qu'une <strong>salle à manger</strong>, <strong>parfaite</strong> pour des moments de <strong>convivialité</strong>.</p><p>La <strong>cuisine</strong> est entièrement <strong>équipée</strong>, comprenant une <strong>plaque de cuisson, un four électrique et une hotte aspirante</strong>, et donne également sur <strong>un séchoir.</strong></p><p>Toujours <strong>au rez-de-chaussée</strong>, vous trouverez également <strong>un séjour</strong> <strong>avec balcon</strong> ainsi qu’une <strong>salle d'eau</strong> pour les <strong>invités</strong> qui complète la partie jour.</p><p> <strong>À l'étage</strong>, l'espace <strong>nuit</strong> comprend une <strong>suite</strong> <strong>parentale avec une salle de</strong> <strong>bain</strong> <strong>privative</strong>. On trouve également <strong>trois chambres à coucher, chacune disposant d'un dressing. Une salle de bain commune</strong> est à disposition.</p><p>Le duplex est équipé par un <strong>chauffage central, une climatisation en split système, une connexion en fibre optique et un système d’alarme.</strong></p><p><strong>Une place de parking permettant d’accueillir deux voitures est disponible au sous-sol.</strong></p> 5 piècesNaNNaNNaN20-30 years
8912190000NaNTUNIS1.02.01.046.0YesnoYesnononoYes0.01.0GAMMARTHMARSA0.0<p>Cet appartement est situé au sein d'une résidence s'étendant sur trois niveaux avec ascenseur, à Gammarth.</p><p>Il se trouve au rez-de-chaussée de la résidence. L'entrée dessert un salon avec une cuisine américaine et un balcon.</p><p>La partie nuit comprend une chambre à coucher et une salle de douche.</p><p>Une place de parking est également disponible à l'intérieur de la résidence</p> 2 piècesNaNse trouve au rez-de-chaussee de la residence.NaN10-20 years
8913210000NaNTUNIS3.03.01.088.0YesnoYesnononoNoNaNNaNCITE EL KHADRACITE EL KHADRA/CITÉ EL KHADRA3.0<p>Cet appartement <strong>S+3</strong> situé au <strong>3ème étage</strong>, doté d'un <strong> point d'eau et une salle de bain</strong>, d'un <strong>balcon</strong> attenant au salon, <strong>trois chambres</strong> avec <strong>dressings</strong> et d'une <strong>cuisine spacieuse</strong>.</p><p>Niché dans un quartier résidentiel paisible, il offre un cadre de vie idéal à proximité des <strong>écoles</strong>, <strong>commerces</strong> et <strong>transports en commun</strong>.</p><p>Une <strong>place de parking</strong> est disponible.</p> 3 piècesNaNNaNNaN30-50 years
8914470000NaNTUNIS2.02.01.0103.0YesnonoYesYesnoYesNaNNaNCENTRE URBAIN NORDCITE EL KHADRA/CITÉ EL KHADRA1.0<p>Un <strong>appartement S+2</strong> situé au Centre Urbain Nord, d'une superficie de <strong>103 m²</strong>, au <strong>1er étage</strong> d'un immeuble. Ce bien se compose d'un <strong>salon spacieux</strong>, de <strong>deux chambres</strong> confortables et d'une <strong>salle de bain</strong> bien <strong>aménagée</strong>. <strong>La cuisine</strong> est entièrement <strong>équipée</strong> avec une <strong>plaque de cuisson</strong>, une <strong>hotte aspirante</strong>, un <strong>four</strong> et un <strong>sèche-linge.</strong> L'appartement bénéficie également de la <strong>climatisation centrale</strong> et du <strong>chauffage central</strong> pour un confort optimal tout au long de l'année. De plus, il dispose d'une <strong>place de parking</strong> dédiée. Il offre un cadre de vie agréable avec toutes les commodités nécessaires à proximité.</p> 2 piècesNaNNaNNaN5-10 years
8915267000NaNBEN AROUS2.03.01.090.0YesnoYesYesnonoNoNaNNaNBOU MHELBOU MHEL EL BASSATINE1.0<p>Appartement S+2 en <strong>direct promoteur</strong> situé dans une résidence en <strong>R+4 à Boumhel El Bassatine</strong>. Plusieurs typologies sont disponibles et les finitions sont en cours avec une <strong>remise des clés</strong> prévue pour <strong>juin 2025</strong>. Cet appartement comprend une entrée menant à un salon <strong>spacieux</strong> revêtu de marbre. La cuisine entièrement <strong>équipée</strong> et <strong>aménagée</strong> dispose également d'un <strong>séchoir</strong>. La partie nuit se compose de deux chambres l'une avec un <strong>dressing</strong> et l'autre donnant accès à une <strong>terrasse</strong>. Une salle de bain commune est à disposition. De plus, <strong>une place de parking</strong> est proposée au prix de 18 000TND.</p> 3 piècesNaNNaNNaNLess than a year
8916490000NaNARIANA1.03.01.0152.0YesnoYesYesYesnoNoNaNNaNENNASREL MNIHLA1.0<p>Ce duplex fait partie d’une <strong>résidence calme et sécurisée à Ennasr 2</strong>. Il bénéficie d'une<strong> entrée indépendante</strong> ainsi qu’un accès à <strong>une terrasse.</strong> Le premier niveau abrite <strong>une chambre à coucher équipée d’un dressing ainsi qu’une salle d’eau</strong>. <strong>Quelques marches nous mènent au rez-de-chaussée qui loge une pièce de vie accueillante,</strong> elle permet d’aménager <strong>un salon et une salle à manger</strong>. <strong>La cuisine</strong> est totalement indépendante, elle est équipée d<strong>’une plaque de cuisson et une hotte aspirante</strong>, elle est complétée par <strong>un séchoir fonctionnel.</strong> Le premier étage comprend <strong>deux chambres à coucher avec dressing</strong> et elles se partagent <strong>une salle de bain avec jacuzzi.</strong> Ce duplex est équipé <strong>d’un chauffage central et d'une climatisation en split système</strong>. <strong>Une place de parking</strong> est disponible pour les futurs occupants.</p> 3 piècesNaNNaNNaN10-20 years
8917385000NaNARIANA3.03.01.0139.0YesnoYesYesYesnoYes1.00.0ENNASREL MNIHLANaN<p>Cet appartement est situé au <strong>sixième</strong> étage d'une résidence <strong>sécurisée</strong>. Dès votre arrivée, un <strong>hall</strong> d'entrée <strong>accueillant</strong> équipée d’un dressing vous y attend. Le salon <strong>lumineux</strong> offre un espace <strong>agréable</strong>. La cuisine <strong>soigneusement</strong> équipée et <strong>aménagée</strong>, elle dispose d'une <strong>plaque</strong> de <strong>cuisson</strong> et d'un <strong>four électrique</strong>, ainsi que d'un espace dédié pour un <strong>séchoir</strong>. <strong>Une salle de douche</strong> complète la partie jour. La partie nuit de l'appartement comprend une <strong>suite parentale</strong> avec <strong>une salle de bain</strong>, ainsi que <strong>deux chambres à coucher dotée d'un dressing chacune</strong> et dont une complétée par <strong>un balcon</strong>. L’appartement est doté <strong>d’un chauffage central et d’une climatisation par split système. </strong><strong>Une place de parking</strong> est également disponible pour les futurs propriétaires.</p> 3 piècesNaNsitue au <strong>sixieme</strong> etage d'une residence <strong>securisee</strong>.NaN10-20 years
8918320000NaNTUNIS3.04.01.0125.0YesnoYesnoYesnoNoNaNNaNCITE EL KHADRACITE EL KHADRA/CITÉ EL KHADRA1.0<p>Cet appartement de type S+3, situé au <strong>premier étage</strong>, comprend <strong>trois chambres</strong> ainsi qu'un <strong>salon spacieux</strong> avec <strong>un balcon vast</strong>e et un point d'eau, une salle de bain, et une cuisine lumineuse.</p><p>Niché dans un quartier résidentiel calme , il offre un cadre de vie idéal à proximité des <strong>écoles</strong>, <strong>commerces</strong> et <strong>transports en commun</strong>.</p><p>Une <strong>place de parking</strong> collective.</p> 4 piècesNaNNaNNaN30-50 years
8919340000NaNTUNIS2.03.01.085.0YesnononononoYes0.00.0GAMMARTHMARSANaN<p>Cet appartement est situé au sein d'une résidence à Gammarth, s'étendant sur deux niveaux et équipée d'un ascenseur.</p><p>L'entrée mène à un hall spacieux qui donne accès au salon avec salle à manger.</p><p>La cuisine dispose d'un séchoir.</p><p>Une salle d'eau est disponible pour les invités dans cette partie jour.</p><p>La partie nuit comprend deux chambres à coucher, chacune dotée de dressings et d'une salle d'eau avec baignoire.</p><p>Une place de parking est également disponible pour les futurs propriétaires.</p> 3 piècesNaNNaNNaN10-20 years